arXiv:2509.19671v3 Announce Type: replace Abstract: Public datasets of Chest X-Rays (CXRs) have long been a popular benchmark for developing machine learning (ML) computer vision models in healthcare. However, the reported strong average-case performance of these models do not necessarily reflect their actual utility when used in heterogeneous clinical settings, potentially masking weaker performance in medically significant scenarios. In this work we use clinical context to provide a more holistic evaluation of models for CXR diagnosis. In particular, we use discharge summaries, recorded prio
The proliferation of AI models in healthcare is prompting a necessary re-evaluation of their real-world efficacy beyond idealized benchmark datasets.
This highlights the critical need for robust, clinically contextualized evaluation of AI models before widespread adoption, impacting both patient safety and investment in healthcare AI.
The standard for validating healthcare AI models is shifting from purely technical performance metrics to include practical clinical utility, potentially impacting research priorities and regulatory pathways.
- · AI model developers specializing in clinical context integration
- · Healthcare institutions prioritizing robust validation
- · Patients benefiting from more reliable AI diagnostics
- · AI models with weak clinical generalizability
- · Developers solely focused on abstract benchmark performance
- · Public health initiatives deploying untested AI at scale
AI models optimized for chest X-ray diagnosis will undergo more rigorous clinical validation.
Increased demand for curated, clinically rich datasets to train and evaluate healthcare AI.
New regulatory frameworks and certification processes for medical AI that emphasize real-world effectiveness over benchmark scores.
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Read at arXiv cs.LG